Variability in Fusarium species Causing Wilt Disease in Crops: A Transcriptomic Approach to Characterize Dialogue Between Host and Pathogen - Crop Improvement: New Approaches and Modern Techniques - page 278

ing the networks of proteins and molecules while interacting from itself as well as
the ones secreted by the host cells (Liu et al. 2010 ). Thus based on systems biology
approach, the network of molecular interaction in Fusarium spp. can provide ad-
ditional insights into the processes of pathogenesis. Moreover, the protein-protein
interaction maps can also provide clues to potential pathogenesis genes and thus
pathogenesis procedure (Zhao et al. 2009 ). The pathogenesis genes are generally
expressed differentially before and after invasion so that the pathogen can sustain or
pass through the host immune system and thus adapt in the host. Similarly the dif-
ferentially expressed genes of F. graminearum may be pathogenic genes (Liu et al.
2010 ). However, false positives may be produced by differentially expressed genes
while identifying genes involved in disease procedures, as some of these genes
are not involved in pathogenesis, despite expression changes during experimenta-
tion. However, by integrating the protein interaction and gene expression studies
on perturbations (drug, extracellular stimuli) (Zhao et al. 2010 ) shall be useful in
identifying the pathogenesis processes. The approaches in systems biology helps
in integrating the protein interaction mapping data and gene expression data and
works on assumptions proteins interacting in a network share similar functions and
it is termed as “Association rule” (Zhao et al. 2010 ) and thus are most probably in-
volved in the similar pathways (Zhao et al. 2010 ). In F. graminearum the prediction
results have shown that most of the pathogenesis genes belong to G-protein coupled
receptors and MAPK signaling pathways (Liu et al. 2010 ) (Table 8.2 ).
Transcriptomics: Understanding of Plant—Microbe
Interactions
Transcript profiling is a functional genomics tool, which is used most widely and
can be conducted along with various genomic tools such as SAGE (serial analysis
of gene expression), MPSS (massive parallel signature sequencing) and microar-
rays (Dilip et al. 2010 ). The transcriptomics of related species (pathogenic and non-
pathogenic) have led to identification of divergence among them in the genome
(Wurtzel et al. 2012 ). The role of regulatory processes in pathogenesis (i.e. the
protein abundance) can be compared with reference proteomics as well as tran-
scriptomics (Voge and Marcotte 2012 ). The transcriptomic approach is necessary
for understanding the pathogenesis processes in particular the receptors present
in the plants which help in recognizing or perceiving the conserved signatures of
pathogens (Schwessinger and Ronald 2012 ). The responses of plants to the biotic
stresses (pathogens) leads to the several changes such as cellular, physiological,
biochemical and most importantly the transcriptomic levels. The interactions are
very complex and are mediated and regulated by the hormonal signaling pathways
(Atkinson and Urwin 2012 ; Tian et al. 2012 ). The modulation of defense systems
by the plants upon the infection, invasion or any other biotic stress can be stud-
ies at the transcriptomic level of interacting host and pathogen (Lee et al. 2012 ;
Tian et al. 2012 ). The metabolomic and transcriptomic studies could be helpful in